PROJECT SUMMARY: BIOSTATISTICS SHARED RESOURCE (BSR) The Biostatistics Shared Resource (BSR) provides collaborative statistical support to Consortium members across each of the Research Programs. We emphasize the importance of establishing ongoing and continuing collaboration with biostatisticians as a means of maximizing scientific collaborations that lead to impactful cancer research. The BSR aids members with projects that do not have dedicated funding for biostatistical support. In addition, the BSR assists members across all Programs in the development of grant proposals. It is expected that such proposals will have biostatistical support built into the budget, in which case ? if a grant is awarded ? a funded collaboration with a biostatistician would subsequently take place outside the auspices of the BSR. In this sense, the BSR frequently spawns NIH-funded research. The BSR is composed of six faculty- level statisticians and three masters-level statisticians, and the CCSG currently funds roughly 1.1 FTE. The level of support for each biostatistician of the BSR ranges from 5-20%, as each biostatistician is primarily funded by research grants and contracts independent of their BSR activities. The CCSG-supported effort ensures that a stable staff of highly skilled biostatisticians is available to Consortium investigators. The members of the BSR have many years of experience working collaboratively with clinical, population, and laboratory scientists and possess a wide range of expertise in statistical methods that are relevant to the Consortium, including clinical trial design (Phase I through Phase III), survival analysis (including competing risks), longitudinal data, diagnostics testing, biomarkers, prediction models, microbiome, genomic, proteomic, metabolomic, high-dimensional data analysis, multi-omic and data-integration methods, analysis of nonlinear time series including modeling and inference with ordinary differential equations, cancer survivorship, inverse- probability weighting, statistical genetics, biological pathway analysis, mixed model and kernel methods, quantile regression, and causal inference.